CN116266248A

CN116266248A - System and method for generating and simulating vehicle events and data

Info

Publication number: CN116266248A
Application number: CN202211552310.5A
Authority: CN
Inventors: S·H·Y·卢德维格; N·I·兰德里; A·舒克拉; G·李
Original assignee: BlackBerry Ltd
Current assignee: BlackBerry Ltd
Priority date: 2021-12-16
Filing date: 2022-12-05
Publication date: 2023-06-20
Also published as: US20230192087A1; EP4198800A1

Abstract

Some embodiments of the present disclosure relate to systems and methods for generating and simulating vehicle events and data. The present disclosure provides, in one aspect, a computer-implemented method for generating data associated with a vehicle event, the method comprising: obtaining vehicle event data for a vehicle event associated with a vehicle; identifying parameters associated with the vehicle event; simulating the parameters based on simulating the vehicle event using the model of the vehicle event data and the parameters, and extrapolating the parameter data based on the simulated vehicle event.

Description

System and method for generating and simulating vehicle events and data

Technical Field

The present disclosure relates generally to generating data for simulating a vehicle (vehicle) event, and more particularly to generating data for modeling parameters associated with a vehicle event for use in supplementing the simulation of the vehicle event.

Background

Adverse vehicle events, such as vehicle collisions and accidents, may present analysis challenges due to limitations in acquiring or collecting data related to the adverse event. Replication adverse events also present challenges due to inherent damage that may be caused to the vehicle, which may place the vehicle in a damaged condition, fail to use the vehicle until repair is completed, or otherwise limit the effectiveness of using the vehicle to replicate the same behavior.

However, it is still desirable to develop further improvements and advancements in connection with modeling, replicating, and generating vehicle events (including analysis thereof) to overcome the shortcomings of the known art and to provide additional advantages thereto.

This section is intended to introduce various aspects of the art that may be associated with the present disclosure. This discussion is believed to be helpful in providing a framework to facilitate a better understanding of the particular aspects of the present disclosure. Accordingly, it should be understood that this section should be read from this perspective and is not necessarily an admission of prior art.

Drawings

Embodiments of the present disclosure will now be described, by way of example only, with reference to the accompanying drawings.

FIG. 1 is a flow chart of an embodiment of a method for generating new data for parameters associated with a vehicle event according to the present disclosure.

FIG. 2 is a flow chart of an embodiment of a method for generating new data for parameters associated with a vehicle event, including re-simulating and analyzing the vehicle event based on the new parameter data, in accordance with the present disclosure.

FIG. 3 is a flow chart of an embodiment of a method according to the present disclosure, including modifying and updating a vehicle event data set, and including further generating new data for parameters associated with a vehicle event.

FIG. 4 is a flow chart of an embodiment of a method according to the present disclosure, including modifying and updating a vehicle event data set during vehicle simulation, and including further generating new data for parameters associated with a vehicle event.

FIG. 5 is a flow chart of an embodiment of a method according to the present disclosure, the method including modifying and updating a vehicle event data set during vehicle simulation, and including further generating new data for parameters associated with a vehicle event, including re-simulating the vehicle event based on the new parameter data and analyzing it.

FIG. 6 is a diagram of an embodiment of the present disclosure for a Graphical User Interface (GUI) for displaying, simulating, modifying, and generating data for a vehicle event.

FIG. 7 is a block diagram of an example computing device or system for implementing systems and methods for generating new vehicle data and events in accordance with the present disclosure.

Throughout the drawings, only one or less than all of the examples of elements visible in the drawings are sometimes indicated by leads and reference numerals for simplicity and to avoid confusion. However, it should be understood that in such a case, all other examples are likewise specified and covered by the corresponding description, in accordance with the corresponding description.

Detailed Description

The following are examples of systems and methods for classifying vehicle locations according to the present disclosure.

According to one aspect, the present disclosure provides a computer-implemented method for generating data associated with a vehicle event, the method comprising: obtaining vehicle event data for a vehicle event associated with a vehicle; identifying parameters associated with the vehicle event; simulating the parameters based on the model using the vehicle event data and the parameters; and extrapolating the parameter data based on the simulated vehicle event.

According to an example embodiment, the vehicle event data comprises modifiable driving parameters, the method further comprising: modifying driving parameter data associated with the modifiable driving parameter, and updating the vehicle event data based on the modified driving parameter data.

According to an example embodiment, modifying the driving parameter data includes: during simulation of the vehicle event, modified driving parameter data is received from a simulation device configured to replicate the modifiable driving parameter.

According to an example embodiment, the modifiable driving parameter is a direction of the vehicle and the simulation device is a steering wheel.

According to an example embodiment, the modifiable driving parameter is a speed of the vehicle and the simulation device includes an accelerator pedal and a brake pedal.

According to an example embodiment, the modifiable driving parameter includes at least one of: the direction of the vehicle and the speed of the vehicle, and the simulation device is a video game controller.

According to an example embodiment, generating data associated with the vehicle event further comprises: updating the vehicle event data to include the extrapolated parameter data, and re-simulating the vehicle event using the updated vehicle event data.

According to an example embodiment, generating data associated with the vehicle event further comprises: the re-simulated vehicle event is compared to the simulated vehicle event to determine a margin of error for the re-simulated vehicle event.

According to an example embodiment, the re-simulated vehicle event has a confidence interval of 90% or higher.

According to an example embodiment, the vehicle event is a collision involving a vehicle, a runaway of the vehicle, a vehicle acceleration, a vehicle deceleration, a change in vehicle direction, a vehicle blind spot check, a vehicle reverse or a vehicle park.

According to an example embodiment, the parameter associated with the vehicle event is a vehicle cabin parameter, a weather condition or a road condition.

According to an example embodiment, the vehicle event data comprises a plurality of vehicle event data segments, the method further comprising: identifying a first vehicle event data segment for a first vehicle event and a second vehicle event data segment for a second vehicle event from the vehicle event data, wherein a state of the vehicle is continuous between the first vehicle event data segment and the second vehicle event data segment, and modifying the vehicle event data to include the first vehicle event data segment and the second vehicle event data segment.

According to one aspect, the present disclosure provides a non-transitory computer-readable medium having instructions stored thereon that, when executed by a computing device, cause the computing device to perform a method for generating data associated with a vehicle event, the method comprising: obtaining vehicle event data for a vehicle event associated with a vehicle; identifying parameters associated with the vehicle event; simulating the parameters based on the model using the vehicle event data and the parameters; and extrapolating the parameter data based on the simulated vehicle event.

According to an example embodiment, the vehicle event data includes modifiable driving parameters, and generating data associated with the vehicle event further includes: modifying driving parameter data associated with the modifiable driving parameter, and updating the vehicle event data based on the modified driving parameter data.

According to an example embodiment, the vehicle event data comprises a plurality of vehicle event data segments, and generating data associated with the vehicle event further comprises: identifying, from the vehicle event data, a first vehicle event data segment for a first vehicle event and a second vehicle event data segment for a second vehicle event, wherein the first vehicle event data segment and the second vehicle event data segment form a continuous vehicle event; and modifying the vehicle event data to include the first vehicle event data segment and the second vehicle event data segment.

The vehicle simulation and data generation systems and methods disclosed herein generally utilize data sets obtained from vehicle events for generating new data or data sets for the vehicle events. Vehicle events include, but are not limited to: vehicle collisions, vehicle accidents, blind spot checks, mirror or proximity checks, vehicle driving patterns or maneuvers (including acceleration, braking, maintaining speed, reversing, turning, parking and/or shifting). The generated data may be used for various additional purposes for analyzing the vehicle, the vehicle event, and/or one or more parameters of the vehicle or the vehicle event, including, but not limited to: supplementing the original dataset with new data to fill in gaps or errors in the original dataset, generating new data for uncharacterized parameters in the original dataset, generating data for characterizing different conditions of the vehicle event, and/or may be otherwise used to re-simulate the vehicle event with the newly generated data for further analysis thereof. For example, the newly generated data may modify grip (grip) between the vehicle and the road based on weather conditions (e.g., dry and wet), where re-simulating vehicle events under different grip conditions may provide more in-depth insight into vehicle performance as a function of weather. As another example, the new data may characterize process parameters not included in the raw data set, such as generating data for seat sensors, to provide insight into the condition of the seat occurring during a vehicle event. The raw data set may further contain newly generated data and vehicle events may be re-simulated to further evaluate the vehicle under new and/or different parameters, conditions, and scenarios. Examples of parameters for which data may be generated include, but are not limited to: speed, acceleration, braking, engine RPM, vehicle orientation, tire grip, fuel level, battery level, airbag sensors, proximity sensors, seat sensors, geographic location (such as latitude and longitude that may be obtained or generated from a Global Positioning System (GPS)), camera data in the vehicle, heating ventilation and air conditioning (Heating Ventilation and Air Conditioning, HVAC) settings, door sensors, electric Vehicle (EV) parameters such as range of charge and Battery Management Status (BMS), window sensors, tire pressure, ignition status, headlamp status, windshield wiper status, antilock Brake System (ABS) status, tracking control status, lane drift, safety belt status, steering inputs, gear selector, differential (differential) status, and status of various driver assistance devices.

The raw data set corresponding to the vehicle event typically includes data sufficient to replicate the driving pattern of the vehicle event, such as modifiable driving parameters. For example, the raw data sets may include engine torque, brake pedal actuation, accelerator pedal actuation, gear selection, relative tire tread, steering angle offset, traction control, ABS status, vehicle speed, engine RPM, vehicle orientation (e.g., pitch, roll, and yaw), engine Horsepower (HP), and engine displacement (displacement). In one embodiment, the raw data sets for duplicating the driving mode of the vehicle for the vehicle event include ignition state, traction control state, ABS state, accelerator input, brake input, gear selector position, transmission type (e.g., automatic or manual), steering input, steering angle offset, drive type (e.g., all-wheel drive (AWD), front-wheel drive (FWD) or rear-wheel drive (RWD)), vehicle position, vehicle heading (e.g., yaw or azimuth), powertrain battery pack output current, powertrain battery pack temperature, engine/battery coolant temperature, battery pack discharge rate, alternator load, minimum/maximum battery voltage, maximum battery current, and minimum/maximum battery temperature, these data sets may be obtained directly from the vehicle for example, such data may be obtained from a vehicle equipped with a sensor system having a speed sensor, steering wheel sensor, accelerometer, gyroscope, brake sensor, and/or other sensor for generating data characterizing the driving mode of the vehicle for the vehicle event. For generating new data according to the present disclosure. Vehicle event data may be obtained or modified in real-time according to embodiments of the present disclosure. In one embodiment, the hardware simulation includes a replicated vehicle cabin and a plurality of replicated controls for simulating data generation of the vehicle.

Embodiments according to the present disclosure may further include features for gambling. For example, the vehicle event may be updated to include new or additional data and/or parameters in the vehicle event data set. The updated vehicle event data set may be further simulated or analyzed to evaluate vehicle performance and/or various vehicle parameters associated with the updated vehicle event. Analysis of the re-simulation event may include margin of error and confidence intervals, which may further be used as a basis for generating a game score to gamify an updated/modified vehicle event data set for comparison of one data set with another.

Fig. 1 illustrates a method 100 for generating new vehicle event data for a vehicle event in accordance with one embodiment of the present disclosure. The operation of method 100 is not intended to be limiting, but rather is intended to illustrate one example of generating new vehicle event data for a vehicle event. In some embodiments, the method 100 may be implemented with one or more additional operations not described, and/or without one or more of the described operations. Similarly, the order in which the operations of the method 100 are illustrated and described below is not intended to be limiting, but rather an illustration of one example of generating new vehicle event data in accordance with the present disclosure.

In some embodiments, the method 100 may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a computing network implemented in the cloud, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices that perform some or all of the operations of method 100 in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured by hardware, firmware, and/or software to be specifically designed to perform one or more of the operations of method 100.

Operation 110 may include obtaining vehicle event data characterizing a driving pattern of a vehicle of an associated vehicle event. Vehicle events include, but are not limited to: vehicle collisions, vehicle accidents, blind spot checks, mirror or proximity checks, vehicle driving patterns or maneuvers (including acceleration, braking, maintaining speed, reversing, turning, parking and/or shifting). The vehicle event data may be obtained directly from the vehicle, may have been previously saved to and obtained from a storage medium, or may otherwise be obtained or supplemented by other means, such as by copying accelerator pedal, brake pedal, steering wheel, and/or other simulation sensors using hardware or software simulations, for generating supplemental data for characterizing the driving pattern of the vehicle during the associated vehicle event. The vehicle event data obtained in operation 110 may be input to operation 130 for obtaining a model of parameters associated with the vehicle event and/or operation 140 for simulating the vehicle event.

Operation 110 may also be based on vehicle event data from a plurality of overlapping vehicle events. For example, the vehicle event data may include a first vehicle event data segment corresponding to a first vehicle event; and may include a second vehicle event data segment corresponding to a second vehicle event. For example, the first vehicle event may correspond to a first driving pattern related to movement from a starting vehicle position to a first vehicle position; and, the second vehicle event may correspond to a second driving pattern related to movement from the starting vehicle position to the second vehicle position, wherein the first vehicle event and the second vehicle event overlap each other from the starting vehicle position to the intermediate position. In one embodiment, the intermediate position may occur before either the first position or the second position. In one embodiment, the intermediate position may be the first position or the second position. In one embodiment, the first location and the second location are the same location. In one embodiment, from the starting position to the intermediate position, the first vehicle driving mode may be the same as the second vehicle driving mode.

As a further example, the vehicle event data may include a third vehicle event data segment corresponding to a third vehicle event. For example, the third vehicle event may correspond to a third driving mode related to movement from the neutral position to a third vehicle position. In one embodiment, the third location may be different from each of the first and second vehicle locations. In one embodiment, the third location may be the same as the first location and/or the second location. Thus, embodiments of a vehicle event data set according to the present disclosure may include multiple segments of vehicle event data sharing overlapping portions or data points for combining together to evaluate different variables and results related to a vehicle event, such as, for example, combining together in operation 140 to simulate different aspects or segments of a vehicle event.

Operation 120 may include identifying parameters associated with a vehicle event for which additional data is desired. In one embodiment, the vehicle event data may include a partial or incomplete data set of parameters, whereby additional data for the parameters may be required. In one embodiment, the vehicle event data does not include data for parameters. Examples of parameters of a vehicle event include, but are not limited to: speed, acceleration, braking, engine RPM, vehicle orientation, tire grip, fuel level, battery level, airbag sensors, proximity sensors, seat sensors, geographic location (such as latitude and longitude that may be obtained or generated from a Global Positioning System (GPS)), camera data in the vehicle, heating Ventilation and Air Conditioning (HVAC) settings, door sensors, electric Vehicle (EV) parameters such as charge mileage range and Battery Management Status (BMS), window sensors, tire pressure, ignition status, headlamp status, windshield wiper status, antilock Brake System (ABS) status, tracking control status, lane drift, seat belt status, steering inputs, gear selector, differential status, and status of various driver assistance devices.

Operation 130 may include identifying a model of the parameters identified in operation 120. The model of the parameters may be used as an input to operation 140 for simulating a vehicle event based on the vehicle event data obtained in operation 110 and the model of the parameters obtained in operation 130. The model of the parameters may be used in conjunction with vehicle event data as a basis for simulating the parameters during simulation of the vehicle event and generating therefrom new parameter data for the desired parameters. The model of the parameters may be derived in a number of ways. For example, operation 130 may include: the vehicle event data obtained in operation 110 is received and pattern recognition is applied to the vehicle event data to build a pattern or model of the expected behavior of the parameter. For example, the vehicle event data may have gaps, errors, or omissions in vehicle speed, and pattern recognition may be applied to the vehicle event data to build a pattern or model of vehicle speed based on other data in the vehicle event data set. Or as a further example, operation 130 may be used to extrapolate or model the pattern for expanding the vehicle data over a larger period of time. In one embodiment, expanding the vehicle data over a greater period of time may include expanding the data to traverse a reverse route to complete a round trip between two locations. For example, the vehicle event data may include data of a vehicle event that encompasses a vehicle traversing from a first location to a second location; and, the extension data may include: based on the state of the vehicle at the second location, the vehicle event data is expanded to encompass traversal of the vehicle from the second location to the first location. The model and/or new data may further be provided to operation 140 for modeling and generating the new data. In one embodiment, operation 130 includes applying a machine learning algorithm to the vehicle event data to generate a model of the parameters and/or to generate data of the parameters. In one embodiment, operation 130 may be applied to a subset of vehicle event data, such as a first vehicle event data segment corresponding to a first driving pattern of a first vehicle event. In one embodiment, operation 130 includes generating new data to be used as input for operation 140.

In one embodiment, operation 130 may receive a vehicle data set having a discontinuity. For example, the vehicle event data may include a data gap between consecutive vehicle event segments, such as a gap between a first vehicle event data segment and a second vehicle event data segment. Operation 130 may generate a model of the vehicle event based on the first and second vehicle event data segments, for example, using a machine learning algorithm, to generate new data to bridge a discontinuity or gap between the two data segments.

Operation 140 may include simulating the vehicle event based on the vehicle event data obtained in operation 110 and the model of the parameters obtained in operation 130. In this regard, operation 140 may simulate the parameters based on simulating the vehicle event data with the model of the parameters, thereby providing a basis for generating new data for use in operation 150 of extrapolating the parameter data from the simulated vehicle event. Operation 150 may include extrapolating the parameter data from the simulated vehicle event operation 140 and evaluating statistics of the extrapolated data, such as confidence intervals or margins of error of the extrapolated parameter data. In one embodiment, operation 140 may receive new data from operation 130 based on the new data generated during operation 130 to supplement, replace, or fill in gaps in the data that may be contained in the vehicle data set obtained from operation 110.

Operation 140 may simulate the vehicle event data, either in whole or in part, or based on different pieces of data contained in the vehicle event data set. For example, operation 140 may simulate a first vehicle event based on continuity between the first vehicle event data segment and the third vehicle event data segment; or may simulate the second vehicle event based on continuity between the second vehicle event data segment and the third vehicle event data segment; etc.

Fig. 2 illustrates a method 200 for generating new vehicle event data for a vehicle event, according to one embodiment of the present disclosure. The operation of method 200 is not intended to be limiting, but rather is intended to illustrate one example of generating new vehicle event data for a vehicle event. In some embodiments, the method 200 may be implemented with one or more additional operations not described, and/or without one or more of the described operations. Similarly, the order in which the operations of method 200 are illustrated and described below is not intended to be limiting, but rather an illustration of one example of generating new vehicle event data in accordance with the present disclosure.

In some embodiments, the method 200 may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a computing network implemented in the cloud, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices that perform some or all of the operations of method 200 in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured by hardware, firmware, and/or software to be specifically designed to perform one or more of the operations of method 200.

Method 200 may include operations corresponding to equivalent operations or the same operations of the corresponding operations described with respect to method 100. For example, operation 210 for obtaining vehicle event data, operation 220 for identifying parameters associated with a vehicle event, operation 230 for obtaining a model of parameters associated with a vehicle event, operation 240 for simulating a vehicle event based on the vehicle event data and the model of parameters, and operation 250 for extrapolating parameter data from the simulated vehicle event may be performed at least in accordance with corresponding

operations

110, 120, 130, 140, and 150 of method 100. The method 200 may further include

operations

260, 270, and 280 as described below.

Operation 260 may include updating the vehicle event data to include the extrapolated parameter data obtained in operation 250. The updated vehicle event data may then be used as a basis for further simulation under the

method

100 or 200. For example,

operations

110 and 210 for obtaining vehicle event data, updated vehicle event data may be obtained from operation 260. Advantageously, the updated vehicle event data may then be used as input to simulation operation 140 or operation 240, in combination with simulating the vehicle event for the new or different parameters and parametric models identified in the corresponding operations 120/130 and 220/230. As another example, the updated vehicle event data may simply be re-simulated as part of evaluating statistics of the updated vehicle event data set, such as, for example, evaluating confidence intervals or margins of error of the updated vehicle event data set.

Operation 270 may include re-simulating the vehicle event with the updated vehicle event data obtained from operation 260. The output of the re-simulated vehicle event may be used as an input to operation 280 for comparison with the output of the simulated event from operation 240. Operation 280 may compare the re-simulated vehicle event with the original simulated vehicle event to compare results or determine other analysis information, such as a margin or confidence interval for error of the re-simulated vehicle event. In one embodiment, the output of operation 280 is a score for gambling the statistical nature of the re-simulated vehicle event.

Fig. 3 illustrates a method 300 for generating new vehicle event data for a vehicle event, according to one embodiment of the present disclosure. The operation of method 300 is not intended to be limiting, but rather is intended to illustrate one example of generating new vehicle event data for a vehicle event. In some embodiments, method 300 may be implemented with one or more additional operations not described, and/or without one or more of the described operations. Similarly, the order in which the operations of method 300 are illustrated and described below is not intended to be limiting, but rather an illustration of one example of generating new vehicle event data in accordance with the present disclosure.

In some embodiments, method 300 may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a computing network implemented in the cloud, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices that perform some or all of the operations of method 300 in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured by hardware, firmware, and/or software to be specifically designed to perform one or more of the operations of method 300.

Method 300 may include operations corresponding to equivalent operations or the same operations of the respective operations described with respect to method 100 or method 200. For example, operation 310 for obtaining vehicle event data, operation 320 for identifying parameters associated with a vehicle event, operation 330 for obtaining a model of parameters associated with a vehicle event, operation 340 for simulating a vehicle event based on the vehicle event data and the model of parameters, and operation 350 for extrapolating parameter data from the simulated vehicle event may be performed at least in accordance with corresponding

operations

110, 120, 130, 140, and 150 according to method 100 and

corresponding operations

210, 220, 230, 240, and 250 according to method 200. Method 300 may further include

operations

312 and 314, as described below, and operations from method 200 corresponding to

operations

260, 270, and 280 may be similarly implemented.

The method 300 may include

operations

312 and 314 corresponding to modifying and updating the vehicle event data obtained in operation 310, respectively. Modifying the vehicle event data may include, but is not limited to: the method may include inserting additional data points into the vehicle event data to account for gaps, errors, or omissions in the vehicle event data set, modifying the data to different values (e.g., changing the speed of the vehicle at certain points in time), and/or extending the duration of the vehicle event to cover additional durations by extrapolating the vehicle event data. In one embodiment, modifying the vehicle event data includes visually inspecting, modifying, and generating the vehicle event data using a Graphical User Interface (GUI). In one embodiment, a hardware simulation may be used to generate or insert missing data, for example, a hardware simulation for an accelerator pedal may be used to generate vehicle speed and acceleration data for a vehicle event, to fill in gaps in a vehicle event dataset, to overlay data in a vehicle event dataset, or to generate new data for a vehicle event dataset. After operation 312 is completed, operation 314 may be invoked to update the vehicle event data and further serve as input to operation 340 for simulating a vehicle event using the updated vehicle event data.

Fig. 4 illustrates a method 400 for generating new vehicle event data for a vehicle event in accordance with one embodiment of the present disclosure. The operations of method 400 are not intended to be limiting, but rather are intended to illustrate one example of generating new vehicle event data for a vehicle event. In some embodiments, method 400 may be implemented with one or more additional operations not described, and/or without one or more of the described operations. Similarly, the order in which the operations of method 400 are illustrated and described below is not intended to be limiting, but rather an illustration of one example of generating new vehicle event data in accordance with the present disclosure.

In some embodiments, the method 400 may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a computing network implemented in the cloud, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices that perform some or all of the operations of method 400 in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured by hardware, firmware, and/or software to be specifically designed to perform one or more of the operations of method 400.

Method 400 may include operations corresponding to equivalent operations or identical operations to the respective operations described with respect to method 100, method 200, and/or method 300. For example, operation 410 for obtaining vehicle event data, operation 420 for identifying parameters associated with a vehicle event, operation 430 for obtaining a model of parameters associated with a vehicle event, operation 440 for simulating a vehicle event based on the vehicle event data and the model of parameters, and operation 450 for extrapolating parameter data from the simulated vehicle event may be performed at least in accordance with corresponding

operations

110, 120, 130, 140, and 150 according to method 100 and/or corresponding operations according to method 200 and/or method 300. Method 400 may further include

operations

416 and 418, as described below, and operations from method 200 and method 300, such as

operations

260, 270, 280, 312, and/or 314, may be similarly implemented.

The method 400 may include

operations

416 and 418, the

operations

416 and 418 corresponding to modifying and updating the vehicle event data during operation 440, respectively, of simulating the vehicle event data using the model of the parameter. Modifying the vehicle event data may include, but is not limited to: the method may include inserting additional data points into the vehicle event data to account for gaps, errors, or omissions in the vehicle event data set, modifying the data to different values (e.g., changing the speed of the vehicle at certain points in time), and/or extending the duration of the vehicle event to cover additional durations by extrapolating the vehicle event data.

Operations

416 and 418 may be utilized as needed to change and modify the vehicle event data during the vehicle event simulation of operation 440. Modifying and updating the vehicle event data may be done in real-time or near real-time during simulation, or for example, the vehicle simulation may be suspended during operation 440, providing the opportunity to selectively modify and update the vehicle event data. For example, operation 416 may utilize a hardware or software simulation of the accelerator pedal to generate data of the speed and/or acceleration of the vehicle. The data generated from the simulated accelerator pedal may be used to supplement, replace, or otherwise provide a new data stream for the vehicle event to simulate. As part of operation 416, the simulated accelerator pedal may thus provide updated data during simulation to modify the vehicle event data. In one embodiment, modifying the vehicle event data includes using a Graphical User Interface (GUI) to visually inspect, modify, and generate the vehicle event data, such as GUI 600 described with respect to fig. 6. Operation 418 may update the vehicle event data in real-time or near real-time during the vehicle event simulation of operation 440. Operation 418 may update the vehicle event data in a variety of ways, for example, operation 418 may simply replace and/or update lost or erroneous data with newly generated data. In one embodiment, operation 418 may override existing data in the vehicle event data. In one embodiment, operation 416 creates data for new parameters that are not characterized or omitted from the vehicle event data set, wherein operation 418 may update the vehicle event data set to include the new parameter data.

Fig. 5 illustrates a method 500 for generating new vehicle event data for a vehicle event, according to one embodiment of the present disclosure. The operations of method 500 are not intended to be limiting, but rather are intended to illustrate one example of generating new vehicle event data for a vehicle event. In some embodiments, method 500 may be implemented with one or more additional operations not described, and/or without one or more of the described operations. Similarly, the order in which the operations of method 500 are illustrated and described below is not intended to be limiting, but rather an illustration of one example of generating new vehicle event data in accordance with the present disclosure.

In some embodiments, the method 500 may be implemented in one or more processing devices (e.g., a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a computing network implemented in the cloud, a state machine, and/or other mechanisms for electronically processing information). The one or more processing devices may include one or more devices that perform some or all of the operations of method 500 in response to instructions stored electronically on an electronic storage medium. The one or more processing devices may include one or more devices configured by hardware, firmware, and/or software to be specifically designed to perform one or more of the operations of the method 500.

Method 500 may include operations corresponding to equivalent operations or identical operations to the respective operations described with respect to method 100, method 200, method 300, and/or method 400. For example, operation 510 for obtaining vehicle event data, operation 520 for identifying parameters associated with a vehicle event, operation 530 for obtaining a model of parameters associated with a vehicle event, operation 540 for simulating a vehicle event based on the vehicle event data and the model of parameters, and operation 550 for extrapolating parameter data from the simulated vehicle event may be performed at least in accordance with corresponding

operations

110, 120, 130, 140, and 150 according to method 100 and corresponding operations according to

method

200, 300, and/or 400.

The method 500 may still further include an operation 516 for modifying the vehicle event data and an operation 518 for updating the vehicle event data during the simulated vehicle event of operation 540, the

operations

516 and 518 proceeding in an equivalent or identical manner corresponding to the

respective operations

416 and 418 of the method 400.

The method 500 may also further include an operation 550 for extrapolating the parameter data based on the simulated vehicle event of operation 540, an operation 560 for updating the vehicle event data set with the extrapolated parameter data of operation 550, an operation 570 for re-simulating the vehicle event with the updated vehicle event data set of operation 560, and an operation 580 for determining a margin or other analysis of error of the re-simulated vehicle event, in a manner equivalent to or the same as the corresponding

operations

250, 260, 270, and/or 280 of the method 200.

Fig. 6 is one embodiment of a GUI600 for visually displaying, simulating, controlling, and generating new data of a vehicle event in accordance with one embodiment of the present disclosure, such as in accordance with any of

methods

100, 200, 300, 400, and 500. GUI600 includes a visual representation of vehicle 610 overlaid on a generic grid background 620 for providing a visual representation of vehicle 610 during a vehicle event, as may be visually depicted, for example, during

operations

140, 240, 340, 440, and 550 for simulating a vehicle event or during

operations

270 and 570 for re-simulating a vehicle event with updated vehicle event data. GUI600 may include a timeline 630 and a plurality of GUIs and timeline controls 632, which may include, but are not limited to, controls for loading a vehicle dataset, saving a vehicle dataset, rewinding to a previous point in time during simulation, pausing simulation, and/or stopping simulation. The timeline 630 may include sliders for selectively changing to particular points in time of a vehicle event.

GUI600 may also include a plurality of controls and

corresponding indicators

640, 650, 660, 670 for modifying and visually representing vehicle events, modifying and visually representing parameters and data associated with vehicle events, and/or generating and visually representing new data. For example, the input controls 640 may include controls and visual indicators for acceleration, braking, and steering of the vehicle 610. The visual indicators associated with the input controls 640 may reflect parameters or data contained in the vehicle event data set, and the controls 640 may be used to modify parameters or data in the vehicle event data set, such as by using slider controls or hardware simulations to modify or generate data for acceleration, braking, and/or steering according to one or more operations described herein (such as according to

operations

110, 210, 310, 312, 410, 416, 510, and/or 516). Similarly, gear controls 650 may include controls and visual indicators for park brake, driving status, and transmission status; the start/stop controls 660 may include controls for turning on or off the vehicle 610 (including starting the engine); also, miscellaneous controls 670 may include controls and visual indicators for headlights, windshield wipers, and/or door status. GUI600 may also include a plurality of visual indicators 680, such as visual indicators for engine RPM, visual speed, and fuel status 680 (whether based on gasoline level or battery level).

FIG. 7 is a block diagram of an example computerized device or system 700 that may be used to implement one or more aspects or components of embodiments of systems and methods for generating and simulating vehicle events according to the present disclosure, e.g., to implement one or more operations as described with respect to method 100, method 200, method 300, method 400, and/or method 500; and/or for example, for implementing GUI 600.

Computerized system 700 may include one or more of a processor 702, a memory 704, a mass storage device 710, an input/output (I/O) interface 706, and a communication subsystem 708. Further, the system 700 may include, for example, multiple processors 702 and/or multiple memories 704, etc. Processor 702 may include one or more of a digital processor, an analog processor, a digital circuit designed to process information, an analog circuit designed to process information, a state machine, and/or other mechanisms for electronically processing information. These processing units may be physically located within the same device, or processor 702 may represent processing functions of multiple devices operating in concert. The processor 702 may be configured as by software; hardware; firmware; some combination of software, hardware, and/or firmware, and/or other mechanisms for configuring processing capabilities on the processor 702 to execute modules or to otherwise perform functions attributed to the modules, and may include one or more physical processors, processor-readable instructions, circuit devices, hardware, storage media, or any other component during execution of processor-readable instructions.

One or more of the components or subsystems of computerized system 700 may be interconnected by one or more buses 712 or in any other appropriate manner.

Bus 712 may be one or more of any type of several bus architectures including a memory bus, a memory controller bus, a peripheral bus, and so forth. The CPU 702 may comprise any type of electronic data processor. The memory 704 may include any type of system memory such as Dynamic Random Access Memory (DRAM), static Random Access Memory (SRAM), synchronous DRAM (SDRAM), read-only memory (ROM), combinations thereof, and the like. In one embodiment, the memory may include ROM for use at startup, and DRAM for program and data storage for use when executing programs.

The mass storage device 710 may include any type of storage device configured to store and make accessible data, programs, and other information via the bus 712. The mass storage device 710 may include one or more of a solid state drive, a hard disk drive, a magnetic disk drive, an optical disk drive, and the like. In some embodiments, data, programs, or other information may be stored remotely, for example, in the cloud. Computerized system 700 may send or receive information to a remote storage device in any suitable manner, including via communication subsystem 708 over a network or other data communication medium.

The I/O interface 706 may provide an interface for enabling wired and/or wireless communication between the computerized system 700 and one or more other devices or systems. For example, the I/O interface 706 may be used to communicatively couple with a sensor, such as a camera or video camera. Furthermore, additional or fewer interfaces may be utilized. For example, one or more serial interfaces may be provided, such as a Universal Serial Bus (USB) (not shown).

The computerized system 700 may be used to configure, operate, control, monitor, sense, and/or adjust devices, systems, and/or methods according to the present disclosure.

Communication subsystem 708 may be provided for one or both of sending and receiving signals on any form or medium of digital data communication, including communication networks. Examples of communication networks include a Local Area Network (LAN), a Wide Area Network (WAN), an internetwork such as the internet, and a peer-to-peer network such as an ad hoc peer-to-peer network. Communication subsystem 708 may include any component or collection of components for enabling communication over one or more wired and wireless interfaces. These interfaces may include, but are not limited to, USB, ethernet (e.g., IEEE 802.3), high Definition Multimedia Interface (HDMI), firewire ^TM (e.g. IEEE 1394), thunderbolt ^TM 、WiFi ^TM (e.g., IEEE 802.11), wiMAX (e.g., IEEE 802.16), bluetooth ^TM Or Near Field Communication (NFC), as well as GPRS, UMTS, LTE, LTE-a and Dedicated Short Range Communication (DSRC). Communication subsystem 708 may include one or more ports or other components (not shown) for one or more wired connections. Additionally or alternatively, communication subsystem 708 may include one or more transmitters, receivers, and/or antenna elements (none shown).

The computerized system 700 of fig. 7 is merely an example and is not meant to be limiting. Various embodiments may utilize some or all of the components shown or described. Some embodiments may use other components not shown or described but known to those skilled in the art.

In the previous description, for purposes of explanation, numerous details were set forth in order to provide a thorough understanding of the embodiments. However, it will be apparent to one skilled in the art that these specific details are not required. In other instances, well-known electrical structures and circuits are shown in block diagram form in order to avoid obscuring the understanding. For example, no specific details are provided as to whether the embodiments described herein are implemented as software routines, hardware circuits, firmware, or combinations thereof.

Embodiments of the present disclosure may be represented as a computer program product stored in a machine-readable medium (also referred to as a computer-readable medium, a processor-readable medium, or a computer-usable medium having a computer-readable program code embodied therein). The machine-readable medium may be any suitable tangible, non-transitory medium including magnetic, optical, or electronic storage medium including a diskette, compact disk read only memory (CD-ROM), memory device (volatile or non-volatile), or similar storage mechanism. The machine-readable medium may contain various sets of instructions, code sequences, configuration information, or other data which, when executed, cause a processor to perform steps in a method according to embodiments of the present disclosure. Those skilled in the art will appreciate that other instructions and operations necessary to implement the described embodiments may also be stored on a machine-readable medium. Instructions stored on a machine-readable medium may be executed by a processor or other suitable processing device and may interface with circuitry to perform the described tasks.

The above-described embodiments are intended to be examples only. Alterations, modifications and variations may be effected to the particular embodiments by those of skill in the art without departing from the scope, which is defined solely by the claims appended hereto.

Claims

1. A computer-implemented method for generating data associated with a vehicle event, comprising:

obtaining vehicle event data for a vehicle event associated with a vehicle;

identifying parameters associated with the vehicle event;

simulating the vehicle event based on a model utilizing the vehicle event data and the parameters, simulating the parameters, and

based on the simulated vehicle event, parameter data is extrapolated.

2. The computer-implemented method of claim 1, wherein the vehicle event data comprises modifiable driving parameters, the method further comprising:

modifying driving parameter data associated with the modifiable driving parameter, and

the vehicle event data is updated based on the modified driving parameter data.

3. The computer-implemented method of claim 2, wherein modifying the driving parameter data comprises:

during simulation of the vehicle event, the modified driving parameter data is received from a simulation device configured to replicate the modifiable driving parameter.

4. The computer-implemented method of claim 3, wherein the modifiable driving parameter is a direction of the vehicle and the emulation device is a steering wheel.

5. The computer-implemented method of claim 3, wherein the modifiable driving parameter is a speed of the vehicle and the simulation device includes an accelerator pedal and a brake pedal.

6. The computer-implemented method of claim 3, wherein the modifiable driving parameter includes at least one of: the direction of the vehicle and the speed of the vehicle, and the emulation device is a video game controller.

7. The computer-implemented method of claim 1, further comprising:

updating the vehicle event data to include the extrapolated parameter data, an

The vehicle event is re-simulated using the updated vehicle event data.

8. The computer-implemented method of claim 7, further comprising:

the re-simulated vehicle event is compared to the simulated vehicle event to determine a margin of error for the re-simulated vehicle event.

9. The computer-implemented method of claim 7, wherein the re-simulated vehicle event has a confidence interval of 90% or higher.

10. The computer-implemented method of claim 1, wherein the vehicle event is a collision involving the vehicle, a runaway of the vehicle, a vehicle acceleration, a vehicle deceleration, a change in vehicle direction, a vehicle blind spot check, a vehicle reversing, or a vehicle stopping.

11. The computer-implemented method of claim 1, wherein the parameter associated with the vehicle event is a vehicle cabin parameter, a weather condition, or a road condition.

12. The computer-implemented method of claim 1, wherein the vehicle event data comprises a plurality of vehicle event data segments, the method further comprising:

identifying from the vehicle event data a first vehicle event data segment for a first vehicle event and a second vehicle event data segment for a second vehicle event, wherein a state of the vehicle is continuous between the first vehicle event data segment and the second vehicle event data segment, and

the vehicle event data is modified to include the first vehicle event data segment and the second vehicle event data segment.

13. A non-transitory computer-readable medium having instructions stored thereon that, when executed by a computing device, cause the computing device to perform a method for generating data associated with a vehicle event, comprising:

obtaining vehicle event data for a vehicle event associated with a vehicle;

identifying parameters associated with the vehicle event;

based on the simulated vehicle event, parameter data is extrapolated.

14. The non-transitory computer-readable medium of claim 13, wherein the vehicle event data comprises modifiable driving parameters, and generating data associated with the vehicle event further comprises:

the vehicle event data is updated based on the modified driving parameter data.

15. The non-transitory computer-readable medium of claim 14, wherein modifying the driving parameter data comprises:

16. The non-transitory computer readable medium of claim 15, wherein the modifiable driving parameter includes at least one of: the direction of the vehicle and the speed of the vehicle, and the emulation device is a video game controller.

17. The non-transitory computer-readable medium of claim 13, wherein the vehicle event is a collision involving the vehicle, a runaway of the vehicle, a vehicle acceleration, a vehicle deceleration, a change in vehicle direction, a vehicle blind spot check, a vehicle reverse, or a vehicle park.

18. The non-transitory computer-readable medium of claim 14, wherein the vehicle event data comprises a plurality of vehicle event data segments, and generating data associated with the vehicle event further comprises:

identifying from the vehicle event data a first vehicle event data segment for a first vehicle event and a second vehicle event data segment for a second vehicle event, wherein the first vehicle event data segment and the second vehicle event data segment form a continuous vehicle event, and